September | 2017
Mining is an energy intensive industry with large amounts of energy required to extract the ore, process and ship it to the end customer. Artificial Intelligence, Data Science and Machine Learning can provide additional insights into each of these processes to help mining companies be more energy efficient in their operations.
Decisions made by the mining companies usually have an energy impact whether it be fuel consumption, explosives use, electricity consumption, or energy sourcing. Mining processes are complex, and often decisions are made with incomplete information such as uncertainty in the ore grade, new workforce operating old equipment, people with different levels of training etc.
Energy Variance
How energy is used across the value chain may not be consistent and leads to energy inefficiency. Some causes of energy variance are:
These points highlight some of the issues where the energy consequences of decisions are not always available or apparent to those making them.
Extracting insights from Data
The increase in available data about the equipment, process and the systems provide data which often has energy insights hidden within it. Some of the Data Science and Machine Learning toolsets and approaches will provide insights into the data to extract the impact on energy and the costs of what could happen.
Some examples of using data science to provide value include:
These insights and approaches show how data science tools can uncover insights from numerous data sources captured by the equipment and process. When process and energy data is studied, and analysis is undertaken, insights into the energy use is a key outcome that benefits the business.
However, getting the right data, at the right time at the right resolution for energy efficiency projects can be a challenge. An assessment of the data quality, completeness and availability of energy data is critical to ensure the efficacy of data science & its tools.
Joseph Plant leads Wipro's global Operations Improvement Practice as part of the Natural Resources vertical and has experience in creating, designing and implementing operational improvement solutions for mining and metals customers.
Joseph has a background in industrial automation and software development. He has deep experience in operational systems such as MES (Manufacturing Execution Systems), and leading data science initiatives and analytics for operational systems. Joseph has been involved in creating and delivering numerous energy management initiatives for the mining and metals industry.
Joseph has global experience and has worked on projects and opportunities across the globe including Australia, South Africa, China, Brazil, India, USA, UK, France, Belgium, Finland and Canada.
Joseph can be contacted on LinkedIn: https://au.linkedin.com/in/josephplant
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